plot.logspline: Logspline Density Estimation

In logspline: Routines for Logspline Density Estimation

Logspline Density Estimation

Description

Plots a logspline density, distribution function, hazard function or survival function from a logspline density that was fitted using the 1997 knot addition and deletion algorithm (logspline). The 1992 algorithm is available using the oldlogspline function.

Usage

## S3 method for class 'logspline'
plot(x, n = 100, what = "d", add = FALSE, xlim, xlab = "",
ylab = "", type = "l", ...) 

Arguments

x	logspline object, typically the result of logspline.
n	the number of equally spaced points at which to plot the density.
what	what should be plotted: "d" (density), "p" (distribution function), "s" (survival function) or "h" (hazard function).
add	should the plot be added to an existing plot.
xlim	range of data on which to plot. Default is from the 1th to the 99th percentile of the density, extended by 10% on each end.
xlab, ylab	labels plotted on the axes.
type	type of plot.
...	other plotting options, as desired

Details

This function produces a plot of a logspline fit at n equally spaced points roughly covering the support of the density. (Use xlim = c(from, to) to change the range of these points.)

Author(s)

Charles Kooperberg [e-mail].

References

Charles Kooperberg and Charles J. Stone. Logspline density estimation for censored data (1992). Journal of Computational and Graphical Statistics, 1, 301–328.

Charles J. Stone, Mark Hansen, Charles Kooperberg, and Young K. Truong. The use of polynomial splines and their tensor products in extended linear modeling (with discussion) (1997). Annals of Statistics, 25, 1371–1470.

See Also

logspline, summary.logspline, dlogspline, plogspline, qlogspline, rlogspline,

oldlogspline.

Examples

y <- rnorm(100)
fit <- logspline(y)       
plot(fit) 

logspline documentation built on May 29, 2024, 6:36 a.m.